activity
20242026
collaborators

9 papers

cs.SD2026

Sonalyzer-Moz: A Framework for Analyzing the Structure of Mozart's Sonata Form

Jing Zhao, KokSheik Wong, Vishnu Monn Baskaran +2

The sonata form is a musically rich and hierarchically structured form that poses significant challenges for automatic analysis. While music structure analysis has seen strides of…

cs.LG2026

A Deep Probabilistic Flow-Based Framework for Unsupervised Cross-Domain Soft Sensing

Junn Yong Loo, Hwa Hui Tew, Fang Yu Leong +4

Industrial soft sensing is crucial for accurate process monitoring through reliable inference of dominant sensor variables. However, developing effective data-driven soft sensor mo…

cs.RO2025

Drive As You Like: Multi-Head Diffusion with Reinforcement Learning for Personalized Driving

Fan Ding, Xuewen Luo, Fucai Ke +6

Despite significant progress, imitation learning-based autonomous driving planners remain largely restricted to reproducing high-frequency biased behaviors, overlooking the inheren…

eess.SY2025

Transformer-based Deep Learning Model for Joint Routing and Scheduling with Varying Electric Vehicle Numbers

Jun Kang Yap, Vishnu Monn Baskaran, Wen Shan Tan +3

The growing integration of renewable energy sources in modern power systems has introduced significant operational challenges due to their intermittent and uncertain outputs. In re…

cs.CV2025

SpaRTAN: Spatial Reinforcement Token-based Aggregation Network for Visual Recognition

Quan Bi Pay, Vishnu Monn Baskaran, Junn Yong Loo +2

The resurgence of convolutional neural networks (CNNs) in visual recognition tasks, exemplified by ConvNeXt, has demonstrated their capability to rival transformer-based architectu…

cs.CV2025

Conceptualizing Multi-scale Wavelet Attention and Ray-based Encoding for Human-Object Interaction Detection

Quan Bi Pay, Vishnu Monn Baskaran, Junn Yong Loo +2

Human-object interaction (HOI) detection is essential for accurately localizing and characterizing interactions between humans and objects, providing a comprehensive understanding…